OVERVIEW
Why VDF AI Agents?
VDF AI Agents is more than just another AI tool—it's your complete AI operating system for enterprise value delivery.
PLATFORM CAPABILITIES
Everything Needed to Build and Operate AI Agents
VDF AI Agents includes the execution, tooling, knowledge, observability, and orchestration foundations needed for production use.
Multi-Provider Agent Execution
Connect any LLM or SLM as the agent's model. Mix OpenAI, Anthropic, Azure OpenAI, Mistral, Ollama, and compatible endpoints per agent.
6-Step Agent Builder
Guide teams through a consistent setup flow: Identity → Tools → Behavior → Limits → Review → Deploy.
Tool Registry via MCP
Assign tools from an MCP-powered registry including web search, database query, file reader, calculators, and custom tools.
Knowledge Sources
Connect databases, Slack, GitHub, APIs, and knowledge bases to any agent so every workflow starts from live business context.
Real-Time Performance Dashboard
Monitor executions, latency, success rate, cost, and energy per agent in real time from one operational dashboard.
Network Participation
Register agents for use in VDF AI Networks orchestration flows and promote them from standalone assistants to reusable network nodes.
CORE FEATURES
What Can You Do with VDF AI Agents?
Work smarter with agentic AI that understands your entire ecosystem.
Work Smarter with Agentic AI
Trigger a prompt, delegate a task, or let agents autonomously draft user stories, summarize Zoom calls, search GitHub/Jira semantically, write documentation, and analyze velocity patterns.
Knowledge Sources & Integrations
Connect databases, Slack, GitHub, APIs, Jira, Confluence, GitBook, and Zoom to any agent. Give each workflow the live knowledge sources it needs to read, reason, and act.
Talk, Don't Type
Dictate tasks, notes, or backlog items using Voice Dictation in any language. Perfect for hands-free work in meetings, during transit, or for accessibility.
Multi-Agent Orchestration
Dynamic fallback agents for higher reliability, fine-tuned reasoning across organizational knowledge, configurable orchestration logic, and RAG-powered semantic memory.
Flat Pricing Model
No surprise overage charges, unlimited usage per seat, transparent and scalable model. Whether you're a startup team or enterprise program, your pricing is predictable.
Enterprise-Ready
OAuth-secured integrations for compliance-heavy orgs, audit logging, GDPR and EU data residency support, SSO & Role-based Access Control.
COMPARISON
Copilot-Class, But Not Copilot-Locked
Looking for an open, flexible, and affordable alternative to Copilot? You found it.
| Feature | VDF AI Agents | Microsoft Copilot |
|---|---|---|
| Works with Jira, Slack, GitHub, GitBook, Zoom | Yes | Limited or No |
| Multi-Agent Orchestration | Yes | No |
| Voice Dictation in All Languages | Yes | English-biased |
| Flat Per-Seat Pricing | Yes | Token-based & unclear |
| Manual + Agent Modes | Both | Agent design via Studio only |
| Semantic Search Across Tools | Yes | Yes (limited to Microsoft Graph) |
| On-Premise / Air-Gapped Deployment | Yes — full on-premise | No — Azure cloud only |
| Per-Agent Audit Log & Execution Trace | Yes — every prompt, tool call, output | Partial — Purview logs, no per-step trace |
| Tool-Level Least-Privilege Access | Yes — scoped per agent | Inherits tenant permissions |
| Human Approval Gates for High-Risk Actions | Yes — built into workflow | Limited — manual review only |
| Model Choice (OpenAI, Anthropic, Mistral, Ollama) | Any model or provider | Azure OpenAI only |
| EU AI Act High-Risk System Controls | Immutable logs, approval gates, human oversight | Requires additional tooling |
HOW IT WORKS
Governed Agent Architecture — From Build to Production
Every agent deployed on VDF AI follows a controlled lifecycle where identity, tools, behavior, and limits are defined before any execution begins.
Define → Deploy
The 6-step Agent Builder enforces Identity → Tools → Behavior → Limits → Review → Deploy before any agent goes live. No agent skips the governance checklist.
Learn about AI agent governance →Run → Trace
Every execution is captured as an immutable audit log: input → retrieval hits → tool calls → model response → output. Reconstruct any decision for compliance review or incident investigation.
See on-premise deployment options →Scale → Control
Register agents into VDF AI Networks for multi-agent orchestration. Route each task to the right model, enforce per-node tool permissions, and observe the whole network as one auditable execution graph.
Read about AI agent orchestration →WHAT'S INCLUDED
All in One Enterprise-Ready
All the features and integrations you need to get started.
Platform Evolution
Major platform updates- VDF Agile → now VDF AI Agents
- New Zoom, Slack, and Confluence integrations
- Voice Dictation (all languages)
- GPT-5 reasoning support
Enhanced Capabilities
Improved functionality- Enhanced semantic search for GitHub, GitBook, Jira
- Token-free, flat pricing model
- Improved multi-agent orchestration
- Better enterprise security features
USE CASES
Who is it For?
VDF AI Agents adapts to your team's unique needs and challenges.
Product Teams
- Backlog refinement and story creation
- OKR tracking and alignment
- Automated standup summaries
- Stakeholder communication
Engineering Teams
- Semantic code navigation
- Git PR analysis and insights
- Technical documentation generation
- Code review assistance
Scrum Masters / Agile Coaches
- Sprint health monitoring
- Team performance insights
- Retrospective facilitation
- Agile maturity assessment
Executives
- Delivery pattern analysis
- Performance indicators tracking
- Strategic decision support
- Organizational agility metrics
AGENTS HUB
Build Production-Ready Agents in Six Steps
The Agents Hub is the admin and management portal for VDF AI Agents. Configure each agent through Identity → Tools → Behavior → Limits → Review → Deploy without writing infrastructure code.
Step 1: Identity
Define who the agent is, what it owns, and which model powers it before configuring capabilities.
- Agent name, description, version, and business domain
- Choose any LLM or SLM as the agent's model
- Visibility scope: private, team, or organization
- Category, theme, and ownership metadata
Step 2: Tools
Browse the MCP tool registry and assign capabilities. Agents only use tools you explicitly approve.
- Browse and search the MCP tool registry
- Assign web search, database query, file reader, calculators, and custom tools
- Configure tool-agent associations with parameter overrides
- View tool dependencies and required permissions
Step 3: Behavior
Define how the agent should reason, respond, and use connected knowledge sources. Use LLM-assisted drafting to generate a strong starting point.
- Write system prompts with Monaco-powered editor
- Define response style, escalation rules, and operating instructions
- Attach databases, Slack, GitHub, APIs, and knowledge bases as context
- Template library and LLM-assisted drafting for common agent patterns
Step 4: Limits
Set the operational guardrails that keep the agent predictable, compliant, and cost-aware in production.
- Execution budgets, max turns, and timeout controls
- Tool scopes, approval rules, and restricted actions
- Provider failover, retry behavior, and safe fallback settings
- Cost and energy boundaries per agent or deployment
Step 5: Review
Preview the full agent configuration, validate dependencies, and run a sandbox test before release.
- Full configuration summary with inline editing
- Validation of tools, knowledge sources, and required fields
- Sandbox test run before deployment
- Version history and rollback support
Step 6: Deploy
Publish the agent, monitor it live, and register it for orchestration across the broader VDF platform.
- One-click publish to the organization agent catalog
- Register for use in VDF AI Networks orchestration flows
- Promote versions across environments with rollout controls
- Track executions, latency, success rate, cost, and energy per agent
PROVIDER MANAGEMENT
Any Model. Any Provider. Your Configuration.
Multi-Provider Agent Execution
Connect any LLM or SLM as the agent's model using OpenAI, Anthropic, Azure OpenAI, Mistral, Ollama, or any OpenAI-compatible endpoint.
Fine-Grained Model Parameters
Per-agent temperature, max_tokens, and top_p controls. Configuration is agent-scoped — changes to one agent never affect others.
Provider Failover
Automatic fallback to secondary providers on rate-limit or error. Agents stay operational without manual intervention.
Model Version Pinning
Pin agents to specific model versions for production stability. Test new versions in staging before rolling out to the organization.
PLAYGROUND
Test Agents Before You Deploy Them
Side-by-Side Comparison
Run two agents simultaneously against the same prompt. Compare response quality, latency, and behavior.
- Select any two agents from your catalog
- Submit identical prompts and compare outputs
- Ideal for A/B testing model parameters before production
Interactive Testing Environment
Full chat interface embedded within the admin portal. No context switching — test and refine in one place.
- Thread-based conversation management
- Chat history persisted for regression testing
- Save test sessions as benchmarks
The Playground removes the guesswork from agent tuning. Teams compare outputs directly — without staging environments or separate tooling — reducing agent iteration cycles significantly.
OBSERVABILITY
Real-Time Performance Dashboard for Every Agent Execution
Track executions, latency, success rate, cost, and energy per agent from a single operational view.
Execution Logs
Every agent invocation logged with input, output, model used, latency, token count, success state, and final status.
Session History
Complete audit trail across all user sessions. Filter by agent, user, date range, or execution status.
Performance Metrics
P50/P95 latency, success rate, error rates, throughput, cost, and energy per agent and provider. Identify bottlenecks before users do.
Usage Dashboard
Real-time performance dashboard for operators and teams. Track executions, latency, success rate, cost allocation, and sustainability trends in one place.
Error Tracking
Failed executions surfaced with root cause classification: provider error, tool timeout, context overflow, or rate limit.
Domain & Theme Management
Multi-domain tenant management with dynamic theme overrides per domain. Isolate agent catalogs by business unit.
DOCUMENTATION
Docs Built for the User Experience
Integrated Documentation Viewer
Browse practical guides, connected-app help, and product walkthroughs without leaving the portal.
- Full-text search across end-user documentation
- Product pages for Chat, Agents, Networks, and Data
- Connected-app setup and troubleshooting guides
- Updated alongside the platform experience
VDF AI Documentation
Public documentation covering onboarding, product workflows, integrations, and prompting best practices.
- Getting started and workspace setup guides
- Dedicated pages for Chat, Agents, Networks, and Data
- Integration guides for Google, Microsoft, Jira, Slack, Zoom, and more
- Browse Documentation →
Integrations
Works With Your Stack
VDF AI integrates with the tools you already use, making your workflow more efficient and intelligent.
Related foundational reading
These resources explain the platform category, governance model, and Copilot-alternative framing behind VDF AI Agents.
How the enterprise platform layer differs from a single assistant.
AI Agent GovernanceWhy tool permissions, audit logs, and model policy matter before agents scale.
Microsoft Copilot AlternativeWhen enterprises need more control than a suite-native assistant can offer.
FAQ
Frequently Asked Questions
Everything you need to know about VDF AI Agents.
EXPLORE MORE
Related Resources
Related Agents
Related Tools
Related Use Cases
Related Resources
Validate Your Enterprise AI Use Case
Bring one workflow and we will map it to the agents, tools, governance, and deployment model it needs — so you see what VDF AI Agents changes for your team before you commit.